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import torch
import torch.nn as nn
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
class RotaryEmbedding(nn.Module):
def __init__(self, head_dim: int, max_seq_len: int, theta: float = 10000.0):
super().__init__()
# Match RotaryEmbedding exactly
self.rot_dim = head_dim // 2 # Only half of head_dim is rotated
# Frequency calculation - match RotaryEmbedding exactly
freqs = 1.0 / (theta ** (torch.arange(0, self.rot_dim, 2).float() / self.rot_dim))
t = torch.arange(max_seq_len, dtype=torch.float32).unsqueeze(1)
freqs = t * freqs.unsqueeze(0)
freqs_cis = torch.exp(1j * freqs)
cos_vals = freqs_cis.real
sin_vals = freqs_cis.imag
self.register_buffer('cos_cache', cos_vals, persistent=False)
self.register_buffer('sin_cache', sin_vals, persistent=False)
def apply(self, x: torch.Tensor, position_ids: torch.Tensor) -> torch.Tensor:
"""
Now works with [..., heads, seq_len, head_dim] - no permutation needed!
"""
d = self.rot_dim // 2
# Get cos/sin for positions - shape: [seq_len, d]
cos = self.cos_cache[position_ids] # [seq_len, d]
sin = self.sin_cache[position_ids] # [seq_len, d]
# Broadcast to match x: [..., heads, seq_len, d]
cos = cos.unsqueeze(0).unsqueeze(0) # [1, 1, seq_len, d]
sin = sin.unsqueeze(0).unsqueeze(0) # [1, 1, seq_len, d]
# Split rotated part
xq_r = x[..., :d] # [..., heads, seq_len, d]
xq_i = x[..., d:d*2] # [..., heads, seq_len, d]
# Apply rotation
xq_out_r = xq_r * cos - xq_i * sin
xq_out_i = xq_r * sin + xq_i * cos
# Update in-place
x[..., :self.rot_dim] = torch.stack([xq_out_r, xq_out_i], dim=-1).view(*x.shape[:-1], self.rot_dim)
return x
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